An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data

Battese, George E.; Harter, Rachel; Fuller, Wayne A.

doi:10.2307/2288915

Cited by 212 publications

(384 citation statements)

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“…The base data of x-values and the model equation used for the study were taken from Battese et al (1988). These data contain a sample of segments from 12 north-central counties (`small areas') in the state of Iowa with the following three observations for every segment j of county i: y ij , the number of hectares of corn or soy beans; x 1ij , the number of satellite pixels classi®ed as corn; x 2ij , the number of satellite pixels classi®ed as soy beans.…”

Section: Design Of Simulation Studymentioning

confidence: 99%

“…In Section 6, ®nite sample frequentist properties of the eight methods (naõÈ ve, Kackar and Harville (1984), Prasad and Rao (1990), MCPR, KS-I, KS-II*, MC and MMC) of approximating the prediction MSE are studied by simulations based on real data on county crop areas as reported by Battese et al (1988). As illustrated by Hulting and Harville (1991) and Kass and Steey (1989), there are several theoretical results suggesting that the use of Bayesian predictors and the associated posterior variances may be appropriate under frequentist theory.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Versus Frequentist Measures of Error in Small Area Estimation

Singh

Stukel

Pfeffermann

1998

Journal of the Royal Statistical Society Series B: Statistical Methodology

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This paper compares analytically and empirically the frequentist and Bayesian measures of error in small area estimation. The model postulated is the nested error regression model which allows for random small area effects to represent the joint effect of small area characteristics that are not accounted for by the ®xed regressor variables. When the variance components are known, then, under a uniform prior for the regression coef®cients and normality of the error terms, the frequentist and the Bayesian approaches yield the same predictors and prediction mean-squared errors (MSEs) (de®ned accordingly). When the variance components are unknown, it is common practice to replace the unknown variances by sample estimates in the expressions for the optimal predictors, so that the resulting empirical predictors remain the same under the two approaches. The use of this paradigm requires, however, modi®cations to the expressions of the prediction MSE to account for the extra variability induced by the need to estimate the variance components. The main focus of this paper is to review and compare the modi®cations to prediction MSEs proposed in the literature under the two approaches, with special emphasis on the orders of the bias of the resulting approximations to the true MSEs. Some new approximations based on Monte Carlo simulation are also proposed and compared with the existing methods. The advantage of these approximations is their simplicity and generality. Finite sample frequentist properties of the various methods are explored by a simulation study. The main conclusions of this study are that the use of second-order bias corrections generally yields better results in terms of the bias of the MSE approximations and the coverage properties of con®dence intervals for the small area means. The Bayesian methods are found to have good frequentist properties, but they can be inferior to the frequentist methods. The second-order approximations under both approaches have, however, larger variances than the corresponding ®rst-order approximations which in most cases result in higher MSEs of the MSE approximations.

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Section: Design Of Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Versus Frequentist Measures of Error in Small Area Estimation

Singh

Stukel

Pfeffermann

1998

Journal of the Royal Statistical Society Series B: Statistical Methodology

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“…Model (1) is a natural extension of the univariate nested error regression model of Fuller and Harter (1987), applied by Battese, Harter, and Fuller (1988) to estimate county crop areas.…”

Section: Models and Estimationmentioning

confidence: 99%

Using a Short Screening Scale for Small-Area Estimation of Mental Illness Prevalence for Schools

Zaslavsky

2010

Journal of the American Statistical Association

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We use data collected in the National Comorbidity Survey - Adolescent (NCS-A) to develop a methodology to estimate the small-area prevalence of serious emotional distress (SED) in schools in the United States, exploiting the clustering of the main NCS-A sample by school. The NCS-A instrument includes both a short screening scale, the K6, and extensive diagnostic assessments of the individual disorders and associated impairment that determine the diagnosis of SED. We fitted a Bayesian bivariate multilevel regression model with correlated effects for the probability of SED and a modified K6 score at the individual and school levels. Our results provide evidence for the existence of variation in the prevalence of SED across schools and geographical regions. Although the concordance between the modified K6 scale and SED is only modest for individuals, the school-level random effects for the two measures are strongly correlated. Under this model we obtain a prediction equation for the rate of SED based on the mean K6 score and covariates. This finding supports the feasibility of using short screening scales like the K6 as an alternative to more comprehensive lay assessments in estimating school-level rates of SED. These methods may be applicable to other studies aiming at small-area estimation for geographical units.

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“…One of thesimplest models in common use is the 'nested error unit level regression model', employed originally by Battese et al (1988) for predicting areas under corn and soybeans in 12 counties of the state of Iowa in the U.S. Suppose that the values of concomitant variables x ( l ) , .…”

Section: Cross-sectional Models For Continuous Measurementsmentioning

confidence: 99%

Small Area Estimation‐New Developments and Directions

Pfeffermann

2002

Int Statistical Rev

115

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The purpose of this paper is to provide a critical review of the main advances in small area estimation (SAE) methods in recent years. We also discuss some of the earlier developments, which serve as a necessary background for the new studies. The review focuses on model dependent methods with special emphasis on point prediction of the target area quantities, and mean square error assessments. The new models considered are models used for discrete measurements, time series models and models that arise under informative sampling. The possible gains from modeling the correlations among small area random effects used to represent the unexplained variation of the small area target quantities are examined. For review and appraisal of the earlier methods used for SAE, see Ghosh & Rao (1994).

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An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data

Cited by 212 publications

References 0 publications

Bayesian Versus Frequentist Measures of Error in Small Area Estimation

Bayesian Versus Frequentist Measures of Error in Small Area Estimation

Using a Short Screening Scale for Small-Area Estimation of Mental Illness Prevalence for Schools

Small Area Estimation‐New Developments and Directions

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